Integrating electroencephalography and functional magnetic resonance imaging in epilepsy

Introduction: Presurgical evaluation of patients with epilepsy is one of the areas where electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. Objectives: The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG, it is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis (ICA) and wavelet analysis. Methods: This novel method consists in four fundamental steps: selection of components, reconstruction of EEG signal, selection of channel and wavelet analysis and finally construction of the EEG regressor. After the components of interest have been selected, depending on their power, EEG signal due to IED was reconstructed and its wavelet power was used as a regressor in general linear model (GLM). The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. Results: Analysis of in silico data validates our method, since it reconstructs an EEG regressor virtually coincident with the true one, used to simulate the data wave. In all continuous EEG-fMRI recording sessions obtained a good quality EEG allowing the detection of spontaneous IEDs and the analysis of the related blood oxygenation level dependent (BOLD) activation. Conclusions: Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes